On the other hand, the data-driven approaches collect representative data samples from an apparatus, then train machine learning or deep learning models on the collected samples. For example, Kirchgässner and Wallscheid [1]} setup a data acquisition test bench with a 3-phase PMSM (52 kW) and embedded thermocouples to collect measurements. They recorded all key quantities representing various load profiles through a dSPACE DS1006MC rapid-control-prototyping systemhttps://www.dspace.com/en/pub/home/news/dspace-rapid-prototyping-syste.cfm?nv=nb (for further details about this data acquisition, one can refer to [1]}).
In this line, many solutions have been proposed from basic linear regression models to sophisticated deep neural networks (DNNs). From the literature, it is found that the basic regression models are not robust for PMSMs' high-fluctuating thermo-temporal properties and do not produce accurate results. With the advancement of Deep Learning (DL) technology, the models developed using recurrent architectures, viz. LSTM units, Gated Recurrent Units (GRUs), and Temporal Convolutional Network (TCN) demonstrate robustness and high-level precision for various time-series analytical problems [3]}, [4]}, [1]}, [6]}, [7]}, [8]}, [9]}.
